Overview

Dataset statistics

Number of variables35
Number of observations300
Missing cells1081
Missing cells (%)10.3%
Duplicate rows3
Duplicate rows (%)1.0%
Total size in memory84.4 KiB
Average record size in memory288.0 B

Variable types

Numeric26
Categorical9

Alerts

ERC20_avg_time_between_sent_tnx has constant value ""Constant
ERC20_avg_time_between_rec_tnx has constant value ""Constant
ERC20_avg_time_between_rec_2_tnx has constant value ""Constant
ERC20_avg_time_between_contract_tnx has constant value ""Constant
ERC20_min_val_sent_contract has constant value ""Constant
ERC20_max_val_sent_contract has constant value ""Constant
ERC20_avg_val_sent_contract has constant value ""Constant
Dataset has 3 (1.0%) duplicate rowsDuplicates
Avg_min_between_sent_tnx is highly overall correlated with max_val_sent and 1 other fieldsHigh correlation
Avg_min_between_received_tnx is highly overall correlated with Time_Diff_between_first_and_last_(Mins)High correlation
Time_Diff_between_first_and_last_(Mins) is highly overall correlated with Avg_min_between_received_tnx and 4 other fieldsHigh correlation
min_value_received is highly overall correlated with avg_val_received and 11 other fieldsHigh correlation
max_value_received is highly overall correlated with avg_val_received and 4 other fieldsHigh correlation
avg_val_received is highly overall correlated with min_value_received and 5 other fieldsHigh correlation
min_val_sent is highly overall correlated with min_value_received and 3 other fieldsHigh correlation
max_val_sent is highly overall correlated with Avg_min_between_sent_tnx and 7 other fieldsHigh correlation
avg_val_sent is highly overall correlated with min_value_received and 6 other fieldsHigh correlation
total_Ether_sent is highly overall correlated with Avg_min_between_sent_tnx and 7 other fieldsHigh correlation
total_ether_received is highly overall correlated with max_value_received and 4 other fieldsHigh correlation
Total_ERC20_tnxs is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 12 other fieldsHigh correlation
ERC20_total_Ether_received is highly overall correlated with min_value_received and 6 other fieldsHigh correlation
ERC20_total_ether_sent is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_sent_addr is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_rec_addr is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 7 other fieldsHigh correlation
ERC20_uniq_rec_contract_addr is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 7 other fieldsHigh correlation
ERC20_min_val_rec is highly overall correlated with ERC20_avg_val_recHigh correlation
ERC20_max_val_rec is highly overall correlated with min_value_received and 6 other fieldsHigh correlation
ERC20_avg_val_rec is highly overall correlated with min_value_received and 7 other fieldsHigh correlation
ERC20_min_val_sent is highly overall correlated with ERC20_total_ether_sent and 4 other fieldsHigh correlation
ERC20_max_val_sent is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_avg_val_sent is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_sent_token_name is highly overall correlated with Total_ERC20_tnxs and 5 other fieldsHigh correlation
ERC20_uniq_rec_token_name is highly overall correlated with Time_Diff_between_first_and_last_(Mins) and 7 other fieldsHigh correlation
ERC20_total_Ether_sent_contract is highly imbalanced (96.3%)Imbalance
ERC20_uniq_sent_addr.1 is highly imbalanced (96.3%)Imbalance
Total_ERC20_tnxs has 47 (15.7%) missing valuesMissing
ERC20_total_Ether_received has 47 (15.7%) missing valuesMissing
ERC20_total_ether_sent has 47 (15.7%) missing valuesMissing
ERC20_total_Ether_sent_contract has 47 (15.7%) missing valuesMissing
ERC20_uniq_sent_addr has 47 (15.7%) missing valuesMissing
ERC20_uniq_rec_addr has 47 (15.7%) missing valuesMissing
ERC20_uniq_sent_addr.1 has 47 (15.7%) missing valuesMissing
ERC20_uniq_rec_contract_addr has 47 (15.7%) missing valuesMissing
ERC20_avg_time_between_sent_tnx has 47 (15.7%) missing valuesMissing
ERC20_avg_time_between_rec_tnx has 47 (15.7%) missing valuesMissing
ERC20_avg_time_between_rec_2_tnx has 47 (15.7%) missing valuesMissing
ERC20_avg_time_between_contract_tnx has 47 (15.7%) missing valuesMissing
ERC20_min_val_rec has 47 (15.7%) missing valuesMissing
ERC20_max_val_rec has 47 (15.7%) missing valuesMissing
ERC20_avg_val_rec has 47 (15.7%) missing valuesMissing
ERC20_min_val_sent has 47 (15.7%) missing valuesMissing
ERC20_max_val_sent has 47 (15.7%) missing valuesMissing
ERC20_avg_val_sent has 47 (15.7%) missing valuesMissing
ERC20_min_val_sent_contract has 47 (15.7%) missing valuesMissing
ERC20_max_val_sent_contract has 47 (15.7%) missing valuesMissing
ERC20_avg_val_sent_contract has 47 (15.7%) missing valuesMissing
ERC20_uniq_sent_token_name has 47 (15.7%) missing valuesMissing
ERC20_uniq_rec_token_name has 47 (15.7%) missing valuesMissing
Avg_min_between_sent_tnx has 143 (47.7%) zerosZeros
Avg_min_between_received_tnx has 95 (31.7%) zerosZeros
Time_Diff_between_first_and_last_(Mins) has 33 (11.0%) zerosZeros
min_value_received has 92 (30.7%) zerosZeros
max_value_received has 36 (12.0%) zerosZeros
avg_val_received has 36 (12.0%) zerosZeros
min_val_sent has 115 (38.3%) zerosZeros
max_val_sent has 84 (28.0%) zerosZeros
avg_val_sent has 84 (28.0%) zerosZeros
total_Ether_sent has 84 (28.0%) zerosZeros
total_ether_received has 36 (12.0%) zerosZeros
total_ether_balance has 36 (12.0%) zerosZeros
Total_ERC20_tnxs has 94 (31.3%) zerosZeros
ERC20_total_Ether_received has 98 (32.7%) zerosZeros
ERC20_total_ether_sent has 221 (73.7%) zerosZeros
ERC20_uniq_sent_addr has 221 (73.7%) zerosZeros
ERC20_uniq_rec_addr has 98 (32.7%) zerosZeros
ERC20_uniq_rec_contract_addr has 98 (32.7%) zerosZeros
ERC20_min_val_rec has 154 (51.3%) zerosZeros
ERC20_max_val_rec has 103 (34.3%) zerosZeros
ERC20_avg_val_rec has 103 (34.3%) zerosZeros
ERC20_min_val_sent has 236 (78.7%) zerosZeros
ERC20_max_val_sent has 222 (74.0%) zerosZeros
ERC20_avg_val_sent has 222 (74.0%) zerosZeros
ERC20_uniq_sent_token_name has 221 (73.7%) zerosZeros
ERC20_uniq_rec_token_name has 98 (32.7%) zerosZeros

Reproduction

Analysis started2023-04-11 23:23:35.883007
Analysis finished2023-04-11 23:25:33.179983
Duration1 minute and 57.3 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

Avg_min_between_sent_tnx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct156
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3399.8487
Minimum0
Maximum190984.02
Zeros143
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:33.260532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.695
Q3207.09
95-th percentile12105.255
Maximum190984.02
Range190984.02
Interquartile range (IQR)207.09

Descriptive statistics

Standard deviation16164.379
Coefficient of variation (CV)4.7544405
Kurtosis74.892726
Mean3399.8487
Median Absolute Deviation (MAD)2.695
Skewness7.9975144
Sum1019954.6
Variance2.6128714 × 108
MonotonicityNot monotonic
2023-04-11T20:25:33.373847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143
47.7%
18.96 2
 
0.7%
21.68 2
 
0.7%
21.65 1
 
0.3%
155.51 1
 
0.3%
3263.49 1
 
0.3%
350.24 1
 
0.3%
30.63 1
 
0.3%
28.84 1
 
0.3%
2317.25 1
 
0.3%
Other values (146) 146
48.7%
ValueCountFrequency (%)
0 143
47.7%
1.39 1
 
0.3%
1.77 1
 
0.3%
2.06 1
 
0.3%
2.07 1
 
0.3%
2.32 1
 
0.3%
2.61 1
 
0.3%
2.66 1
 
0.3%
2.73 1
 
0.3%
2.93 1
 
0.3%
ValueCountFrequency (%)
190984.02 1
0.3%
110527.4 1
0.3%
100277.04 1
0.3%
93298.56 1
0.3%
77025.79 1
0.3%
46082.48 1
0.3%
42309.29 1
0.3%
27016.12 1
0.3%
23534.96 1
0.3%
21727.78 1
0.3%

Avg_min_between_received_tnx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct200
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5264.3794
Minimum0
Maximum209346.03
Zeros95
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:33.509943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median128.72
Q32361.2375
95-th percentile26429.496
Maximum209346.03
Range209346.03
Interquartile range (IQR)2361.2375

Descriptive statistics

Standard deviation19688.398
Coefficient of variation (CV)3.7399277
Kurtosis66.669678
Mean5264.3794
Median Absolute Deviation (MAD)128.72
Skewness7.5482479
Sum1579313.8
Variance3.8763303 × 108
MonotonicityNot monotonic
2023-04-11T20:25:33.611805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95
31.7%
0.82 6
 
2.0%
0.21 2
 
0.7%
34190.91 1
 
0.3%
142.74 1
 
0.3%
651.31 1
 
0.3%
2.01 1
 
0.3%
2742 1
 
0.3%
9528.54 1
 
0.3%
1801.75 1
 
0.3%
Other values (190) 190
63.3%
ValueCountFrequency (%)
0 95
31.7%
0.02 1
 
0.3%
0.03 1
 
0.3%
0.06 1
 
0.3%
0.07 1
 
0.3%
0.14 1
 
0.3%
0.2 1
 
0.3%
0.21 2
 
0.7%
0.29 1
 
0.3%
0.42 1
 
0.3%
ValueCountFrequency (%)
209346.03 1
0.3%
185539.87 1
0.3%
128280.54 1
0.3%
80425.83 1
0.3%
49455.66 1
0.3%
44867.83 1
0.3%
44699.56 1
0.3%
43069.21 1
0.3%
39069.62 1
0.3%
39025.39 1
0.3%

Time_Diff_between_first_and_last_(Mins)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct264
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119541.05
Minimum0
Maximum1098915.1
Zeros33
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:33.722132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1104.9625
median11871.845
Q3174768.08
95-th percentile597026.5
Maximum1098915.1
Range1098915.1
Interquartile range (IQR)174663.12

Descriptive statistics

Standard deviation215989.38
Coefficient of variation (CV)1.8068219
Kurtosis5.8656461
Mean119541.05
Median Absolute Deviation (MAD)11871.845
Skewness2.4421808
Sum35862314
Variance4.6651412 × 1010
MonotonicityNot monotonic
2023-04-11T20:25:33.831215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
11.0%
56.87 2
 
0.7%
65.03 2
 
0.7%
324.73 2
 
0.7%
198901.02 2
 
0.7%
64.95 1
 
0.3%
29160.87 1
 
0.3%
291451.1 1
 
0.3%
216042.92 1
 
0.3%
256618.77 1
 
0.3%
Other values (254) 254
84.7%
ValueCountFrequency (%)
0 33
11.0%
1.87 1
 
0.3%
4.18 1
 
0.3%
5.72 1
 
0.3%
6.2 1
 
0.3%
7.98 1
 
0.3%
8.8 1
 
0.3%
9.42 1
 
0.3%
12.73 1
 
0.3%
21.3 1
 
0.3%
ValueCountFrequency (%)
1098915.1 1
0.3%
994962.07 1
0.3%
960129.8 1
0.3%
956179.85 1
0.3%
955324.02 1
0.3%
931417.2 1
0.3%
900382.7 1
0.3%
882857.8 1
0.3%
864149.97 1
0.3%
787773.73 1
0.3%

min_value_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct144
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.278343
Minimum0
Maximum2435.6107
Zeros92
Zeros (%)30.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:33.940263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0337995
Q31.43391
95-th percentile101
Maximum2435.6107
Range2435.6107
Interquartile range (IQR)1.43391

Descriptive statistics

Standard deviation161.21891
Coefficient of variation (CV)5.5064217
Kurtosis168.33349
Mean29.278343
Median Absolute Deviation (MAD)0.0337995
Skewness11.849861
Sum8783.503
Variance25991.536
MonotonicityNot monotonic
2023-04-11T20:25:34.049845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92
30.7%
101 20
 
6.7%
0.5 11
 
3.7%
0.1 9
 
3.0%
0.01 7
 
2.3%
0.001 6
 
2.0%
1 4
 
1.3%
0.005 3
 
1.0%
1.5 3
 
1.0%
0.49 3
 
1.0%
Other values (134) 142
47.3%
ValueCountFrequency (%)
0 92
30.7%
1 × 10-61
 
0.3%
1.2 × 10-51
 
0.3%
0.000396 1
 
0.3%
0.000441 1
 
0.3%
0.0005 1
 
0.3%
0.000888 1
 
0.3%
0.000926 1
 
0.3%
0.001 6
 
2.0%
0.001052 1
 
0.3%
ValueCountFrequency (%)
2435.610738 1
 
0.3%
525.303927 1
 
0.3%
525.016521 1
 
0.3%
522.970276 1
 
0.3%
517.428338 1
 
0.3%
512.388539 1
 
0.3%
511.779514 1
 
0.3%
504.190985 1
 
0.3%
101 20
6.7%
50.311864 1
 
0.3%

max_value_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.31421
Minimum0
Maximum18390.842
Zeros36
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:34.164821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.59375
median3.5219915
Q330.036967
95-th percentile260.59525
Maximum18390.842
Range18390.842
Interquartile range (IQR)29.443217

Descriptive statistics

Standard deviation1284.3264
Coefficient of variation (CV)6.4762199
Kurtosis144.57029
Mean198.31421
Median Absolute Deviation (MAD)3.5219915
Skewness11.247639
Sum59494.263
Variance1649494.4
MonotonicityNot monotonic
2023-04-11T20:25:34.272570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
12.0%
101 20
 
6.7%
5 8
 
2.7%
10 8
 
2.7%
1 8
 
2.7%
100 3
 
1.0%
2 3
 
1.0%
0.5 3
 
1.0%
0.2 3
 
1.0%
13 2
 
0.7%
Other values (199) 206
68.7%
ValueCountFrequency (%)
0 36
12.0%
0.011 1
 
0.3%
0.016 1
 
0.3%
0.016549 1
 
0.3%
0.025 1
 
0.3%
0.052892 1
 
0.3%
0.06551 1
 
0.3%
0.081312 1
 
0.3%
0.086235 1
 
0.3%
0.1 2
 
0.7%
ValueCountFrequency (%)
18390.84202 1
0.3%
8892.185276 1
0.3%
7000 1
0.3%
4271.378867 1
0.3%
1496.809015 1
0.3%
1489.94 1
0.3%
1489.220486 1
0.3%
1488.611461 1
0.3%
1483.571662 1
0.3%
1478.029724 1
0.3%

avg_val_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct212
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.048975
Minimum0
Maximum3333.6667
Zeros36
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:34.387931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.241333
median1.0796845
Q311.452046
95-th percentile101
Maximum3333.6667
Range3333.6667
Interquartile range (IQR)11.210714

Descriptive statistics

Standard deviation249.48465
Coefficient of variation (CV)4.7029118
Kurtosis104.60726
Mean53.048975
Median Absolute Deviation (MAD)1.0796845
Skewness9.1132909
Sum15914.693
Variance62242.592
MonotonicityNot monotonic
2023-04-11T20:25:34.492960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
12.0%
50.5 22
 
7.3%
101 20
 
6.7%
1000.5 7
 
2.3%
1 4
 
1.3%
0.1 2
 
0.7%
5 2
 
0.7%
0.64 2
 
0.7%
0.5 2
 
0.7%
0.350668 1
 
0.3%
Other values (202) 202
67.3%
ValueCountFrequency (%)
0 36
12.0%
1 × 10-51
 
0.3%
0.010355 1
 
0.3%
0.012319 1
 
0.3%
0.016549 1
 
0.3%
0.021499 1
 
0.3%
0.025 1
 
0.3%
0.02505 1
 
0.3%
0.026686 1
 
0.3%
0.041487 1
 
0.3%
ValueCountFrequency (%)
3333.666667 1
 
0.3%
1000.5 7
 
2.3%
978.199868 1
 
0.3%
250.562245 1
 
0.3%
113.420657 1
 
0.3%
101 20
6.7%
91.397466 1
 
0.3%
86.524912 1
 
0.3%
74.041111 1
 
0.3%
58.551009 1
 
0.3%

min_val_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct163
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9471577
Minimum0
Maximum101
Zeros115
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:34.606080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.062025
Q31.706975
95-th percentile16.999299
Maximum101
Range101
Interquartile range (IQR)1.706975

Descriptive statistics

Standard deviation8.8233924
Coefficient of variation (CV)2.993865
Kurtosis57.272566
Mean2.9471577
Median Absolute Deviation (MAD)0.062025
Skewness6.4761019
Sum884.14731
Variance77.852254
MonotonicityNot monotonic
2023-04-11T20:25:35.109585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115
38.3%
1 10
 
3.3%
0.5 5
 
1.7%
0.01 4
 
1.3%
0.001 3
 
1.0%
3 3
 
1.0%
0.999139 2
 
0.7%
0.1 2
 
0.7%
2 2
 
0.7%
4.526875 1
 
0.3%
Other values (153) 153
51.0%
ValueCountFrequency (%)
0 115
38.3%
7 × 10-51
 
0.3%
8 × 10-51
 
0.3%
0.000198 1
 
0.3%
0.001 3
 
1.0%
0.001078 1
 
0.3%
0.001759 1
 
0.3%
0.002 1
 
0.3%
0.002559 1
 
0.3%
0.004 1
 
0.3%
ValueCountFrequency (%)
101 1
0.3%
52.444836 1
0.3%
49.137992 1
0.3%
35.449712 1
0.3%
32.86571 1
0.3%
31.806779 1
0.3%
26.37 1
0.3%
25.587227 1
0.3%
24.99909 1
0.3%
23.6799 1
0.3%

max_val_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.91033
Minimum0
Maximum34447
Zeros84
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:35.223813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q328.671
95-th percentile190.29008
Maximum34447
Range34447
Interquartile range (IQR)28.671

Descriptive statistics

Standard deviation2601.8803
Coefficient of variation (CV)9.2294607
Kurtosis148.38324
Mean281.91033
Median Absolute Deviation (MAD)2
Skewness12.109159
Sum84573.1
Variance6769781.3
MonotonicityNot monotonic
2023-04-11T20:25:35.331988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
28.0%
10 3
 
1.0%
2 2
 
0.7%
1.1 2
 
0.7%
4 2
 
0.7%
0.999139 2
 
0.7%
101 2
 
0.7%
74.44992 1
 
0.3%
19.874192 1
 
0.3%
2.694659 1
 
0.3%
Other values (200) 200
66.7%
ValueCountFrequency (%)
0 84
28.0%
0.02 1
 
0.3%
0.031846 1
 
0.3%
0.039509 1
 
0.3%
0.04 1
 
0.3%
0.07 1
 
0.3%
0.089 1
 
0.3%
0.09688 1
 
0.3%
0.11 1
 
0.3%
0.1164 1
 
0.3%
ValueCountFrequency (%)
34447 1
0.3%
28903.57479 1
0.3%
2696.633909 1
0.3%
1758.263098 1
0.3%
1519.64391 1
0.3%
1470.620309 1
0.3%
1452.455404 1
0.3%
1452.353989 1
0.3%
1452.034351 1
0.3%
1452.029105 1
0.3%

avg_val_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.554648
Minimum0
Maximum2474.2691
Zeros84
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:35.444452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.0826765
Q314.324573
95-th percentile60.707347
Maximum2474.2691
Range2474.2691
Interquartile range (IQR)14.324573

Descriptive statistics

Standard deviation177.53999
Coefficient of variation (CV)4.8568375
Kurtosis122.66446
Mean36.554648
Median Absolute Deviation (MAD)1.0826765
Skewness9.91765
Sum10966.394
Variance31520.446
MonotonicityNot monotonic
2023-04-11T20:25:35.559208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
28.0%
33.666149 15
 
5.0%
50.499487 14
 
4.7%
666.999528 6
 
2.0%
33.666163 5
 
1.7%
0.999139 2
 
0.7%
25.249475 2
 
0.7%
33.666143 2
 
0.7%
0.289836 1
 
0.3%
2.694659 1
 
0.3%
Other values (168) 168
56.0%
ValueCountFrequency (%)
0 84
28.0%
1.2 × 10-51
 
0.3%
2.9 × 10-51
 
0.3%
0.004558 1
 
0.3%
0.010066 1
 
0.3%
0.016978 1
 
0.3%
0.02 1
 
0.3%
0.020471 1
 
0.3%
0.03285 1
 
0.3%
0.052099 1
 
0.3%
ValueCountFrequency (%)
2474.269105 1
 
0.3%
769.595763 1
 
0.3%
666.999528 6
2.0%
500.249528 1
 
0.3%
250.561804 1
 
0.3%
114.219105 1
 
0.3%
99.019293 1
 
0.3%
91.396636 1
 
0.3%
70.143758 1
 
0.3%
63.230318 1
 
0.3%

total_Ether_sent
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct179
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1620.7575
Minimum0
Maximum337852.54
Zeros84
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:35.670039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.6913321
Q3100.99544
95-th percentile1015.9979
Maximum337852.54
Range337852.54
Interquartile range (IQR)100.99544

Descriptive statistics

Standard deviation19879.104
Coefficient of variation (CV)12.265317
Kurtosis276.43118
Mean1620.7575
Median Absolute Deviation (MAD)2.6913321
Skewness16.375095
Sum486227.26
Variance3.9517878 × 108
MonotonicityNot monotonic
2023-04-11T20:25:35.783996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
28.0%
100.998446 15
 
5.0%
100.9989738 14
 
4.7%
2000.998585 6
 
2.0%
100.998488 5
 
1.7%
100.9984278 2
 
0.7%
0.999139 2
 
0.7%
6.66577835 1
 
0.3%
19.87419248 1
 
0.3%
2.69465915 1
 
0.3%
Other values (169) 169
56.3%
ValueCountFrequency (%)
0 84
28.0%
0.02 1
 
0.3%
0.031846428 1
 
0.3%
0.0395093 1
 
0.3%
0.08188331 1
 
0.3%
0.09688 1
 
0.3%
0.098551 1
 
0.3%
0.11797166 1
 
0.3%
0.13164093 1
 
0.3%
0.203740991 1
 
0.3%
ValueCountFrequency (%)
337852.5401 1
 
0.3%
51980.14237 1
 
0.3%
42062.57479 1
 
0.3%
13906.12561 1
 
0.3%
10000.94855 1
 
0.3%
2000.998585 6
2.0%
2000.998113 1
 
0.3%
1420.414967 1
 
0.3%
1332.7314 1
 
0.3%
1097.941133 1
 
0.3%

total_ether_received
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1882.209
Minimum0
Maximum342106.95
Zeros36
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:35.903398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1784292
median15.257632
Q3101
95-th percentile1608.5447
Maximum342106.95
Range342106.95
Interquartile range (IQR)99.821571

Descriptive statistics

Standard deviation20328.012
Coefficient of variation (CV)10.800082
Kurtosis265.0225
Mean1882.209
Median Absolute Deviation (MAD)15.257632
Skewness15.911847
Sum564662.7
Variance4.1322809 × 108
MonotonicityNot monotonic
2023-04-11T20:25:36.015314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 44
 
14.7%
0 36
 
12.0%
2001 7
 
2.3%
1 4
 
1.3%
5 2
 
0.7%
0.64 2
 
0.7%
0.1 2
 
0.7%
0.5 2
 
0.7%
0.70133609 1
 
0.3%
67.5269044 1
 
0.3%
Other values (199) 199
66.3%
ValueCountFrequency (%)
0 36
12.0%
0.016549422 1
 
0.3%
0.02071068 1
 
0.3%
0.022 1
 
0.3%
0.085994956 1
 
0.3%
0.086235 1
 
0.3%
0.0936584 1
 
0.3%
0.1 2
 
0.7%
0.11969366 1
 
0.3%
0.13214493 1
 
0.3%
ValueCountFrequency (%)
342106.953 1
 
0.3%
53174.17165 1
 
0.3%
50652.45143 1
 
0.3%
42062.59433 1
 
0.3%
11940.43782 1
 
0.3%
10187.8755 1
 
0.3%
10001 1
 
0.3%
5303.391356 1
 
0.3%
2001 7
2.3%
1587.8892 1
 
0.3%

total_ether_balance
Real number (ℝ)

Distinct195
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.45147
Minimum-1965.6878
Maximum50652.451
Zeros36
Zeros (%)12.0%
Negative24
Negative (%)8.0%
Memory size4.7 KiB
2023-04-11T20:25:36.139636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1965.6878
5-th percentile-0.99613561
Q10.000504
median0.001554
Q30.018004236
95-th percentile242.91898
Maximum50652.451
Range52618.139
Interquartile range (IQR)0.017500235

Descriptive statistics

Standard deviation3008.7356
Coefficient of variation (CV)11.507816
Kurtosis265.89749
Mean261.45147
Median Absolute Deviation (MAD)0.001554
Skewness15.960773
Sum78435.442
Variance9052489.7
MonotonicityNot monotonic
2023-04-11T20:25:36.268543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
12.0%
0.001554 15
 
5.0%
0.001026218 14
 
4.7%
0.000861 12
 
4.0%
0.00105 7
 
2.3%
0.001414909 6
 
2.0%
0.000504 6
 
2.0%
0.001512 5
 
1.7%
0.000483 3
 
1.0%
0.002583 3
 
1.0%
Other values (185) 193
64.3%
ValueCountFrequency (%)
-1965.68779 1
0.3%
-62.01628963 1
0.3%
-32.09915869 1
0.3%
-22.89 1
0.3%
-19.64194018 1
0.3%
-17.96892874 1
0.3%
-17.24072198 1
0.3%
-11.998047 1
0.3%
-11.97960456 1
0.3%
-7.502961453 1
0.3%
ValueCountFrequency (%)
50652.45143 1
0.3%
10187.8755 1
0.3%
5303.391356 1
0.3%
4254.412938 1
0.3%
1587.8892 1
0.3%
1194.029282 1
0.3%
1159.614004 1
0.3%
764.1994771 1
0.3%
624.8973142 1
0.3%
611.3820377 1
0.3%

Total_ERC20_tnxs
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)12.6%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean9.229249
Minimum0
Maximum498
Zeros94
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:36.367198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile43.2
Maximum498
Range498
Interquartile range (IQR)2

Descriptive statistics

Standard deviation41.862146
Coefficient of variation (CV)4.5358129
Kurtosis83.167881
Mean9.229249
Median Absolute Deviation (MAD)1
Skewness8.343603
Sum2335
Variance1752.4393
MonotonicityNot monotonic
2023-04-11T20:25:36.458976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 94
31.3%
1 73
24.3%
2 26
 
8.7%
3 11
 
3.7%
4 9
 
3.0%
5 6
 
2.0%
6 5
 
1.7%
7 3
 
1.0%
9 2
 
0.7%
15 2
 
0.7%
Other values (22) 22
 
7.3%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 94
31.3%
1 73
24.3%
2 26
 
8.7%
3 11
 
3.7%
4 9
 
3.0%
5 6
 
2.0%
6 5
 
1.7%
7 3
 
1.0%
8 1
 
0.3%
9 2
 
0.7%
ValueCountFrequency (%)
498 1
0.3%
263 1
0.3%
246 1
0.3%
155 1
0.3%
123 1
0.3%
97 1
0.3%
95 1
0.3%
75 1
0.3%
68 1
0.3%
59 1
0.3%

ERC20_total_Ether_received
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct96
Distinct (%)37.9%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean6299138.4
Minimum0
Maximum1.5532699 × 109
Zeros98
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:36.573914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.337
Q313.37
95-th percentile235880.91
Maximum1.5532699 × 109
Range1.5532699 × 109
Interquartile range (IQR)13.37

Descriptive statistics

Standard deviation97649331
Coefficient of variation (CV)15.502014
Kurtosis252.93529
Mean6299138.4
Median Absolute Deviation (MAD)1.337
Skewness15.902946
Sum1.593682 × 109
Variance9.5353918 × 1015
MonotonicityNot monotonic
2023-04-11T20:25:36.715233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
32.7%
1.337 36
 
12.0%
13.37 22
 
7.3%
100.337 4
 
1.3%
113.37 2
 
0.7%
5.256290693 1
 
0.3%
2.085559153 1
 
0.3%
0.10638945 1
 
0.3%
32625.13439 1
 
0.3%
3.470693463 1
 
0.3%
Other values (86) 86
28.7%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 98
32.7%
8.48 × 10-141
 
0.3%
1 × 10-121
 
0.3%
1 × 10-111
 
0.3%
4.6 × 10-111
 
0.3%
1.2 × 10-91
 
0.3%
0.022498233 1
 
0.3%
0.033297851 1
 
0.3%
0.10638945 1
 
0.3%
0.615885994 1
 
0.3%
ValueCountFrequency (%)
1553269911 1
0.3%
10376481.72 1
0.3%
10000400 1
0.3%
8224365.159 1
0.3%
5443050.706 1
0.3%
1556556.631 1
0.3%
1141890.121 1
0.3%
701817.7156 1
0.3%
556644.9773 1
0.3%
464323.1929 1
0.3%

ERC20_total_ether_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)11.9%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean90270.657
Minimum0
Maximum10353460
Zeros221
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:36.840235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile33441.824
Maximum10353460
Range10353460
Interquartile range (IQR)0

Descriptive statistics

Standard deviation784695.01
Coefficient of variation (CV)8.6926919
Kurtosis130.84668
Mean90270.657
Median Absolute Deviation (MAD)0
Skewness11.021303
Sum22838476
Variance6.1574626 × 1011
MonotonicityNot monotonic
2023-04-11T20:25:36.944158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 221
73.7%
100 4
 
1.3%
2000 1
 
0.3%
138033.9125 1
 
0.3%
2220.378189 1
 
0.3%
15 1
 
0.3%
20090 1
 
0.3%
10353459.57 1
 
0.3%
22625.1343 1
 
0.3%
116853.7301 1
 
0.3%
Other values (20) 20
 
6.7%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 221
73.7%
1.2 × 10-91
 
0.3%
1.4 × 10-51
 
0.3%
4.95725152 1
 
0.3%
15 1
 
0.3%
20.83224224 1
 
0.3%
36.61203433 1
 
0.3%
100 4
 
1.3%
1216.789506 1
 
0.3%
2000 1
 
0.3%
ValueCountFrequency (%)
10353459.57 1
0.3%
6068738.885 1
0.3%
3378704.893 1
0.3%
1141889.49 1
0.3%
397136.8967 1
0.3%
354504.2989 1
0.3%
261346.6343 1
0.3%
231237.9145 1
0.3%
203335.2956 1
0.3%
138033.9125 1
0.3%

ERC20_total_Ether_sent_contract
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.8%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
252 
5.57e-05
 
1

Length

Max length8
Median length3
Mean length3.0197628
Min length3

Characters and Unicode

Total characters764
Distinct characters6
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 252
84.0%
5.57e-05 1
 
0.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:37.035099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:37.121249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 252
99.6%
5.57e-05 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 505
66.1%
. 253
33.1%
5 3
 
0.4%
7 1
 
0.1%
e 1
 
0.1%
- 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 509
66.6%
Other Punctuation 253
33.1%
Lowercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 505
99.2%
5 3
 
0.6%
7 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 763
99.9%
Latin 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 505
66.2%
. 253
33.2%
5 3
 
0.4%
7 1
 
0.1%
- 1
 
0.1%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 505
66.1%
. 253
33.1%
5 3
 
0.4%
7 1
 
0.1%
e 1
 
0.1%
- 1
 
0.1%

ERC20_uniq_sent_addr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)5.9%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean0.93280632
Minimum0
Maximum43
Zeros221
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:37.190815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.8
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2519173
Coefficient of variation (CV)4.5581994
Kurtosis47.31249
Mean0.93280632
Median Absolute Deviation (MAD)0
Skewness6.3260204
Sum236
Variance18.0788
MonotonicityNot monotonic
2023-04-11T20:25:37.269964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 221
73.7%
1 15
 
5.0%
2 3
 
1.0%
6 2
 
0.7%
15 2
 
0.7%
7 1
 
0.3%
21 1
 
0.3%
43 1
 
0.3%
25 1
 
0.3%
20 1
 
0.3%
Other values (5) 5
 
1.7%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 221
73.7%
1 15
 
5.0%
2 3
 
1.0%
3 1
 
0.3%
5 1
 
0.3%
6 2
 
0.7%
7 1
 
0.3%
12 1
 
0.3%
15 2
 
0.7%
18 1
 
0.3%
ValueCountFrequency (%)
43 1
0.3%
25 1
0.3%
21 1
0.3%
20 1
0.3%
19 1
0.3%
18 1
0.3%
15 2
0.7%
12 1
0.3%
7 1
0.3%
6 2
0.7%

ERC20_uniq_rec_addr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)10.7%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean4.1304348
Minimum0
Maximum153
Zeros98
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:37.356995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile20.8
Maximum153
Range153
Interquartile range (IQR)2

Descriptive statistics

Standard deviation13.893115
Coefficient of variation (CV)3.3635962
Kurtosis59.264193
Mean4.1304348
Median Absolute Deviation (MAD)1
Skewness6.8565339
Sum1045
Variance193.01863
MonotonicityNot monotonic
2023-04-11T20:25:37.448197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 98
32.7%
1 74
24.7%
2 29
 
9.7%
4 13
 
4.3%
3 9
 
3.0%
5 4
 
1.3%
8 3
 
1.0%
6 3
 
1.0%
7 2
 
0.7%
38 1
 
0.3%
Other values (17) 17
 
5.7%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 98
32.7%
1 74
24.7%
2 29
 
9.7%
3 9
 
3.0%
4 13
 
4.3%
5 4
 
1.3%
6 3
 
1.0%
7 2
 
0.7%
8 3
 
1.0%
10 1
 
0.3%
ValueCountFrequency (%)
153 1
0.3%
79 1
0.3%
72 1
0.3%
65 1
0.3%
55 1
0.3%
42 1
0.3%
40 1
0.3%
38 1
0.3%
33 1
0.3%
30 1
0.3%

ERC20_uniq_sent_addr.1
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.8%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
252 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 252
84.0%
1.0 1
 
0.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:37.542771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:37.625245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 252
99.6%
1.0 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 505
66.5%
. 253
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 505
99.8%
1 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 505
66.5%
. 253
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 505
66.5%
. 253
33.3%
1 1
 
0.1%

ERC20_uniq_rec_contract_addr
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)10.3%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean4.6166008
Minimum0
Maximum213
Zeros98
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:37.703103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile22
Maximum213
Range213
Interquartile range (IQR)2

Descriptive statistics

Standard deviation17.229751
Coefficient of variation (CV)3.7321293
Kurtosis89.131463
Mean4.6166008
Median Absolute Deviation (MAD)1
Skewness8.3750156
Sum1168
Variance296.86433
MonotonicityNot monotonic
2023-04-11T20:25:37.786893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 98
32.7%
1 74
24.7%
2 26
 
8.7%
4 11
 
3.7%
3 11
 
3.7%
5 7
 
2.3%
7 3
 
1.0%
6 3
 
1.0%
17 2
 
0.7%
12 2
 
0.7%
Other values (16) 16
 
5.3%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 98
32.7%
1 74
24.7%
2 26
 
8.7%
3 11
 
3.7%
4 11
 
3.7%
5 7
 
2.3%
6 3
 
1.0%
7 3
 
1.0%
10 1
 
0.3%
11 1
 
0.3%
ValueCountFrequency (%)
213 1
0.3%
89 1
0.3%
80 1
0.3%
65 1
0.3%
53 1
0.3%
50 1
0.3%
43 1
0.3%
40 1
0.3%
39 1
0.3%
33 1
0.3%

ERC20_avg_time_between_sent_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:37.873183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:37.952272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_avg_time_between_rec_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:38.014802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:38.093318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_avg_time_between_rec_2_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:38.158877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:38.235655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_avg_time_between_contract_tnx
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:38.299499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:38.377177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_min_val_rec
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct45
Distinct (%)17.8%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean42.103514
Minimum0
Maximum7812
Zeros154
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:38.451558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.337
95-th percentile13.37
Maximum7812
Range7812
Interquartile range (IQR)1.337

Descriptive statistics

Standard deviation506.87263
Coefficient of variation (CV)12.038725
Kurtosis222.26076
Mean42.103514
Median Absolute Deviation (MAD)0
Skewness14.639141
Sum10652.189
Variance256919.86
MonotonicityNot monotonic
2023-04-11T20:25:38.557046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 154
51.3%
1.337 30
 
10.0%
13.37 24
 
8.0%
1 4
 
1.3%
0.428709 1
 
0.3%
0.132913 1
 
0.3%
0.76975 1
 
0.3%
1.705607 1
 
0.3%
0.211799 1
 
0.3%
16.008726 1
 
0.3%
Other values (35) 35
 
11.7%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 154
51.3%
1 × 10-61
 
0.3%
0.022498 1
 
0.3%
0.033298 1
 
0.3%
0.10586 1
 
0.3%
0.106389 1
 
0.3%
0.132913 1
 
0.3%
0.206079 1
 
0.3%
0.211799 1
 
0.3%
0.229197 1
 
0.3%
ValueCountFrequency (%)
7812 1
 
0.3%
2000 1
 
0.3%
400 1
 
0.3%
16.008726 1
 
0.3%
15 1
 
0.3%
13.37 24
8.0%
6.026104 1
 
0.3%
5 1
 
0.3%
3.766315 1
 
0.3%
3.367117 1
 
0.3%

ERC20_max_val_rec
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct83
Distinct (%)32.8%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean6217512.9
Minimum0
Maximum1.553032 × 109
Zeros103
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:38.669144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.337
Q313.37
95-th percentile72221.8
Maximum1.553032 × 109
Range1.553032 × 109
Interquartile range (IQR)13.37

Descriptive statistics

Standard deviation97635675
Coefficient of variation (CV)15.703333
Kurtosis252.97503
Mean6217512.9
Median Absolute Deviation (MAD)1.337
Skewness15.904805
Sum1.5730308 × 109
Variance9.532725 × 1015
MonotonicityNot monotonic
2023-04-11T20:25:38.779099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
34.3%
1.337 37
 
12.3%
13.37 22
 
7.3%
600 6
 
2.0%
99 4
 
1.3%
100 3
 
1.0%
15000 2
 
0.7%
1.705607 1
 
0.3%
2.085559 1
 
0.3%
0.106389 1
 
0.3%
Other values (73) 73
24.3%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 103
34.3%
0.022498 1
 
0.3%
0.033298 1
 
0.3%
0.106389 1
 
0.3%
0.401946 1
 
0.3%
0.403529 1
 
0.3%
0.615886 1
 
0.3%
0.755608 1
 
0.3%
0.775401 1
 
0.3%
0.822304 1
 
0.3%
ValueCountFrequency (%)
1553031969 1
0.3%
10000000 1
0.3%
3414993.809 1
0.3%
1750000 1
0.3%
1530000 1
0.3%
950000 1
0.3%
534600 1
0.3%
450523.0759 1
0.3%
342426.6 1
0.3%
325756.271 1
0.3%

ERC20_avg_val_rec
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct94
Distinct (%)37.2%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean100556.37
Minimum0
Maximum19913717
Zeros103
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:38.899183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.819091
Q313.37
95-th percentile7625.2011
Maximum19913717
Range19913717
Interquartile range (IQR)13.37

Descriptive statistics

Standard deviation1289504.8
Coefficient of variation (CV)12.823701
Kurtosis224.30841
Mean100556.37
Median Absolute Deviation (MAD)0.819091
Skewness14.727997
Sum25440762
Variance1.6628226 × 1012
MonotonicityNot monotonic
2023-04-11T20:25:39.014126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
34.3%
1.337 28
 
9.3%
13.37 22
 
7.3%
0.6685 7
 
2.3%
25.08425 2
 
0.7%
20.0674 2
 
0.7%
56.685 2
 
0.7%
2.085559 1
 
0.3%
0.106389 1
 
0.3%
6525.026877 1
 
0.3%
Other values (84) 84
28.0%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 103
34.3%
0.022498 1
 
0.3%
0.033298 1
 
0.3%
0.106389 1
 
0.3%
0.1778 1
 
0.3%
0.315572 1
 
0.3%
0.400627 1
 
0.3%
0.445667 1
 
0.3%
0.58425 1
 
0.3%
0.589657 1
 
0.3%
ValueCountFrequency (%)
19913716.8 1
0.3%
5000200 1
0.3%
99309.22418 1
0.3%
95157.5101 1
0.3%
59867.56272 1
0.3%
54327.12943 1
0.3%
50834.16797 1
0.3%
46922.85092 1
0.3%
18734.31699 1
0.3%
16589.61927 1
0.3%

ERC20_min_val_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)5.9%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean376.59459
Minimum0
Maximum49666.858
Zeros236
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:39.115881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.6
Maximum49666.858
Range49666.858
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3978.0854
Coefficient of variation (CV)10.56331
Kurtosis130.07958
Mean376.59459
Median Absolute Deviation (MAD)0
Skewness11.361998
Sum95278.432
Variance15825163
MonotonicityNot monotonic
2023-04-11T20:25:39.197168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 236
78.7%
100 4
 
1.3%
2812 1
 
0.3%
0.306034 1
 
0.3%
1216.789506 1
 
0.3%
20.832242 1
 
0.3%
49666.85779 1
 
0.3%
1000 1
 
0.3%
39322.2956 1
 
0.3%
500 1
 
0.3%
Other values (5) 5
 
1.7%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 236
78.7%
0.052769 1
 
0.3%
0.306034 1
 
0.3%
0.49478 1
 
0.3%
1 1
 
0.3%
15 1
 
0.3%
20.832242 1
 
0.3%
100 4
 
1.3%
322.8032 1
 
0.3%
500 1
 
0.3%
ValueCountFrequency (%)
49666.85779 1
 
0.3%
39322.2956 1
 
0.3%
2812 1
 
0.3%
1216.789506 1
 
0.3%
1000 1
 
0.3%
500 1
 
0.3%
322.8032 1
 
0.3%
100 4
1.3%
20.832242 1
 
0.3%
15 1
 
0.3%

ERC20_max_val_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)11.5%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean24716.008
Minimum0
Maximum1948807
Zeros222
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:39.287443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21940
Maximum1948807
Range1948807
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186822.63
Coefficient of variation (CV)7.5587704
Kurtosis82.597143
Mean24716.008
Median Absolute Deviation (MAD)0
Skewness8.9836393
Sum6253150
Variance3.4902694 × 1010
MonotonicityNot monotonic
2023-04-11T20:25:39.384297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 222
74.0%
100 4
 
1.3%
41035.5 1
 
0.3%
25760 1
 
0.3%
2184.300341 1
 
0.3%
15 1
 
0.3%
19900 1
 
0.3%
1948806.991 1
 
0.3%
10000 1
 
0.3%
37091.7838 1
 
0.3%
Other values (19) 19
 
6.3%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 222
74.0%
3 × 10-61
 
0.3%
4.482631 1
 
0.3%
15 1
 
0.3%
18.306 1
 
0.3%
20.832242 1
 
0.3%
100 4
 
1.3%
1216.789506 1
 
0.3%
1500 1
 
0.3%
2184.300341 1
 
0.3%
ValueCountFrequency (%)
1948806.991 1
0.3%
1750000 1
0.3%
1350000 1
0.3%
342426 1
0.3%
325756.271 1
0.3%
164013 1
0.3%
85554 1
0.3%
49666.85779 1
0.3%
44811.975 1
0.3%
41035.5 1
0.3%

ERC20_avg_val_sent
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)11.5%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean3290.3332
Minimum0
Maximum188244.72
Zeros222
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:39.482870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5068.9481
Maximum188244.72
Range188244.72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19361.942
Coefficient of variation (CV)5.8844929
Kurtosis50.285568
Mean3290.3332
Median Absolute Deviation (MAD)0
Skewness6.8617733
Sum832454.31
Variance3.7488482 × 108
MonotonicityNot monotonic
2023-04-11T20:25:39.572598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 222
74.0%
100 4
 
1.3%
5138.620322 1
 
0.3%
5521.3565 1
 
0.3%
69.386818 1
 
0.3%
15 1
 
0.3%
5022.5 1
 
0.3%
188244.7194 1
 
0.3%
5656.283575 1
 
0.3%
5564.463339 1
 
0.3%
Other values (19) 19
 
6.3%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 222
74.0%
1 × 10-61
 
0.3%
0.99145 1
 
0.3%
12.204011 1
 
0.3%
15 1
 
0.3%
20.832242 1
 
0.3%
69.386818 1
 
0.3%
100 4
 
1.3%
1000 1
 
0.3%
1094.10707 1
 
0.3%
ValueCountFrequency (%)
188244.7194 1
0.3%
132378.9656 1
0.3%
114188.949 1
0.3%
102859.9811 1
0.3%
101667.6478 1
0.3%
86633.45879 1
0.3%
49666.85779 1
0.3%
9623.479383 1
0.3%
7542.644658 1
0.3%
5656.283575 1
0.3%

ERC20_min_val_sent_contract
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:39.663527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:39.743846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_max_val_sent_contract
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:39.813975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:39.893010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_avg_val_sent_contract
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.4%
Missing47
Missing (%)15.7%
Memory size4.7 KiB
0.0
253 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters759
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 253
84.3%
(Missing) 47
 
15.7%

Length

2023-04-11T20:25:39.958956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T20:25:40.037973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 253
100.0%

Most occurring characters

ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 506
66.7%
Other Punctuation 253
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 506
100.0%
Other Punctuation
ValueCountFrequency (%)
. 253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 506
66.7%
. 253
33.3%

ERC20_uniq_sent_token_name
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)5.9%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean1.1422925
Minimum0
Maximum58
Zeros221
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:40.101439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.4
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.9351246
Coefficient of variation (CV)5.1958011
Kurtosis64.067453
Mean1.1422925
Median Absolute Deviation (MAD)0
Skewness7.5544352
Sum289
Variance35.225704
MonotonicityNot monotonic
2023-04-11T20:25:40.175866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 221
73.7%
1 13
 
4.3%
2 4
 
1.3%
3 2
 
0.7%
22 2
 
0.7%
4 2
 
0.7%
5 1
 
0.3%
6 1
 
0.3%
56 1
 
0.3%
58 1
 
0.3%
Other values (5) 5
 
1.7%
(Missing) 47
 
15.7%
ValueCountFrequency (%)
0 221
73.7%
1 13
 
4.3%
2 4
 
1.3%
3 2
 
0.7%
4 2
 
0.7%
5 1
 
0.3%
6 1
 
0.3%
10 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
ValueCountFrequency (%)
58 1
0.3%
56 1
0.3%
27 1
0.3%
22 2
0.7%
17 1
0.3%
16 1
0.3%
15 1
0.3%
10 1
0.3%
6 1
0.3%
5 1
0.3%

ERC20_uniq_rec_token_name
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)10.3%
Missing47
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean4.5375494
Minimum0
Maximum211
Zeros98
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-04-11T20:25:40.263879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile22
Maximum211
Range211
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.929932
Coefficient of variation (CV)3.7310738
Kurtosis91.842151
Mean4.5375494
Median Absolute Deviation (MAD)1
Skewness8.5021039
Sum1148
Variance286.62259
MonotonicityNot monotonic
2023-04-11T20:25:40.345013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 98
32.7%
1 74
24.7%
2 27
 
9.0%
3 11
 
3.7%
4 10
 
3.3%
5 7
 
2.3%
7 3
 
1.0%
6 3
 
1.0%
17 2
 
0.7%
12 2
 
0.7%
Other values (16) 16
 
5.3%
(Missing) 47
15.7%
ValueCountFrequency (%)
0 98
32.7%
1 74
24.7%
2 27
 
9.0%
3 11
 
3.7%
4 10
 
3.3%
5 7
 
2.3%
6 3
 
1.0%
7 3
 
1.0%
10 1
 
0.3%
11 1
 
0.3%
ValueCountFrequency (%)
211 1
0.3%
89 1
0.3%
76 1
0.3%
59 1
0.3%
50 1
0.3%
49 1
0.3%
43 1
0.3%
40 1
0.3%
39 1
0.3%
32 1
0.3%

Interactions

2023-04-11T20:25:29.097082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:19.229925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:22.234205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.801823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.931852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:32.302515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:35.049322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:37.914487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:40.120331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.480869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.732625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.662118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:50.205632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.502030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.914719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:58.004004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:01.125074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:04.045912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.956164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:09.147937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.481615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.901838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.702092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:19.347255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:22.094128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:26.364076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:29.190628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:19.393278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:22.338020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.919154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:29.031817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:32.416212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:35.153830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:38.008527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:40.213072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.569650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.835854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.767036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:50.299016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.593065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:55.017348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:58.158016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:01.268938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:04.156279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:07.049865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:09.249285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.577152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.999257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.793822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:19.451193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:22.253310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:26.474661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:29.269140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:19.487327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:22.430739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:25.017105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:29.123372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:32.545704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:35.251427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:38.089826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:40.304255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.649127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.937277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.858270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:50.376964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.669578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:55.108530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:58.273813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:01.384945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:04.268929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:07.131848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:09.334017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.661172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:14.084042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.873054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:19.546039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:22.391855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:26.569295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:29.352247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:19.591295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:22.540853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:25.116951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:29.220065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:32.662194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:35.355672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:38.178279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:40.405817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.730820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:45.042974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.950949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:50.461328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.751784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:55.599158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:58.380282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-04-11T20:25:30.728643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:21.393572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.085938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.217227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:31.174949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.332976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:36.883864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:39.530892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:41.856295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.079971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:46.988269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:49.427341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:51.889870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.253253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.201621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.358881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.358155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.300133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:08.539884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:10.839293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.231214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.030217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:18.658723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.172860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:25.342189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:28.377994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:30.818863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:21.538233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.180028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.334410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:31.344213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.442888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:36.981086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:39.619119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:41.952174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.170857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.083907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:49.537917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:51.980186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.351829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.318874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.470249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.465950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.398756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:08.630071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:10.944216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.326015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.129981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:18.779167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.320435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:25.485159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:28.484083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:30.909836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:21.661114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.282158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.440885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:31.578800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.544913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:37.075945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:39.705266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.046940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.275435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.184187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:49.649300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.074168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.455852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.442258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.574063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.568636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.494503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:08.719306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.039133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.422120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.235432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:18.891164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.468557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:25.687087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:28.594536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:30.990456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:21.772375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.369267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.537426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:31.759993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.646062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:37.166017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:39.786451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.131966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.364559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.275996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:49.790661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.152986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.549334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.557882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.665569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.668853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.580411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:08.798862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.124345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.508947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.324287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:18.978692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.615801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:25.824924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:28.684387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:31.084698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:21.905916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.480877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.639968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:31.924697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.750823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:37.259931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:39.874035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.224509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.464435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.384145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:49.911230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.249051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.655979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.664279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.770124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.766112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.682307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:08.887237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.219347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.625826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.420164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:19.081854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.750005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:25.996167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:28.792323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:31.167891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:22.027397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.589212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.744944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:32.081340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.850188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:37.740886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:39.961312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.312918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.568925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.484875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:50.023290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.336979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.744856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.811913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.874928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.863948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.774877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:08.981188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.310359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.728633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.518456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:19.174235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.878808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:26.117735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:28.907144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:31.249868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:22.138023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:24.702823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:28.840219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:32.195474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:34.955851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:37.831305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:40.044332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:42.399883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:44.656932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:47.577046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:50.114485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:52.420444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:54.833236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:24:57.916144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:00.998461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:03.950034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:06.865196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:09.067988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:11.397748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:13.818873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:16.612741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:19.260155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:21.987276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:26.253081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-04-11T20:25:29.015288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-04-11T20:25:40.452036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_rec_contract_addrERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_uniq_sent_token_nameERC20_uniq_rec_token_nameERC20_total_Ether_sent_contractERC20_uniq_sent_addr.1
Avg_min_between_sent_tnx1.0000.2570.2870.2920.2990.2700.0810.5220.4330.5890.2560.0500.0740.0600.3720.3740.0570.060-0.3190.0570.0290.1810.3650.3640.3750.0590.0000.000
Avg_min_between_received_tnx0.2571.0000.808-0.3460.063-0.019-0.254-0.118-0.175-0.0470.2140.4670.4020.3360.2470.2490.3980.398-0.0410.3160.2960.0940.2620.2610.2490.3990.0000.000
Time_Diff_between_first_and_last_(Mins)0.2870.8081.000-0.2990.1270.032-0.217-0.044-0.1070.0300.3290.4590.5670.4420.3420.3470.5640.563-0.0640.4170.3900.1410.3480.3460.3470.5630.3580.358
min_value_received0.292-0.346-0.2991.0000.4970.6390.7050.7410.7770.6960.285-0.137-0.615-0.559-0.165-0.163-0.608-0.606-0.383-0.537-0.534-0.101-0.169-0.168-0.162-0.6070.0000.000
max_value_received0.2990.0630.1270.4971.0000.9520.3560.7250.7010.7110.9010.264-0.182-0.1850.0300.031-0.164-0.165-0.442-0.173-0.184-0.0320.0440.0430.029-0.1640.0000.000
avg_val_received0.270-0.0190.0320.6390.9521.0000.4360.7360.7340.7100.8220.188-0.332-0.318-0.050-0.049-0.309-0.311-0.437-0.304-0.310-0.067-0.039-0.040-0.051-0.3090.0000.000
min_val_sent0.081-0.254-0.2170.7050.3560.4361.0000.6450.7420.5860.152-0.162-0.493-0.435-0.355-0.355-0.475-0.472-0.168-0.411-0.411-0.202-0.347-0.347-0.356-0.4710.0000.000
max_val_sent0.522-0.118-0.0440.7410.7250.7360.6451.0000.9800.9770.559-0.155-0.315-0.2850.1200.121-0.311-0.307-0.450-0.265-0.2770.0240.1240.1230.120-0.3070.0000.000
avg_val_sent0.433-0.175-0.1070.7770.7010.7340.7420.9801.0000.9470.522-0.165-0.391-0.348-0.003-0.003-0.383-0.379-0.409-0.327-0.335-0.0260.0040.004-0.004-0.3790.0000.000
total_Ether_sent0.589-0.0470.0300.6960.7110.7100.5860.9770.9471.0000.584-0.139-0.261-0.2310.1820.184-0.256-0.254-0.463-0.216-0.2290.0310.1880.1860.183-0.2530.0000.000
total_ether_received0.2560.2140.3290.2850.9010.8220.1520.5590.5220.5841.0000.422-0.036-0.0840.0580.060-0.011-0.015-0.384-0.087-0.098-0.0510.0720.0700.058-0.0120.0000.000
total_ether_balance0.0500.4670.459-0.1370.2640.188-0.162-0.155-0.165-0.1390.4221.0000.1360.025-0.173-0.1750.1470.143-0.0870.005-0.0100.010-0.157-0.155-0.1750.1450.0000.000
Total_ERC20_tnxs0.0740.4020.567-0.615-0.182-0.332-0.493-0.315-0.391-0.261-0.0360.1361.0000.9070.5130.5150.9770.9780.2460.8790.8530.2830.5030.5020.5150.9770.0000.000
ERC20_total_Ether_received0.0600.3360.442-0.559-0.185-0.318-0.435-0.285-0.348-0.231-0.0840.0250.9071.0000.4420.4400.9140.9160.4450.9910.9850.2300.4500.4490.4390.9180.0000.000
ERC20_total_ether_sent0.3720.2470.342-0.1650.030-0.050-0.3550.120-0.0030.1820.058-0.1730.5130.4421.0000.9980.4050.411-0.1450.4380.4250.6800.9860.9860.9980.4090.0000.000
ERC20_uniq_sent_addr0.3740.2490.347-0.1630.031-0.049-0.3550.121-0.0030.1840.060-0.1750.5150.4400.9981.0000.4090.415-0.1500.4350.4200.6680.9820.9811.0000.4120.0000.000
ERC20_uniq_rec_addr0.0570.3980.564-0.608-0.164-0.309-0.475-0.311-0.383-0.256-0.0110.1470.9770.9140.4050.4091.0000.9990.2600.8900.8640.1550.3990.3970.4090.9990.0000.000
ERC20_uniq_rec_contract_addr0.0600.3980.563-0.606-0.165-0.311-0.472-0.307-0.379-0.254-0.0150.1430.9780.9160.4110.4150.9991.0000.2550.8910.8650.1540.4040.4010.4161.0000.0000.000
ERC20_min_val_rec-0.319-0.041-0.064-0.383-0.442-0.437-0.168-0.450-0.409-0.463-0.384-0.0870.2460.445-0.145-0.1500.2600.2551.0000.4520.506-0.003-0.138-0.136-0.1510.2560.0000.000
ERC20_max_val_rec0.0570.3160.417-0.537-0.173-0.304-0.411-0.265-0.327-0.216-0.0870.0050.8790.9910.4380.4350.8900.8910.4521.0000.9930.2350.4540.4520.4340.8930.0000.000
ERC20_avg_val_rec0.0290.2960.390-0.534-0.184-0.310-0.411-0.277-0.335-0.229-0.098-0.0100.8530.9850.4250.4200.8640.8650.5060.9931.0000.2340.4410.4400.4190.8680.0000.000
ERC20_min_val_sent0.1810.0940.141-0.101-0.032-0.067-0.2020.024-0.0260.031-0.0510.0100.2830.2300.6800.6680.1550.154-0.0030.2350.2341.0000.6940.7000.6610.1540.0000.000
ERC20_max_val_sent0.3650.2620.348-0.1690.044-0.039-0.3470.1240.0040.1880.072-0.1570.5030.4500.9860.9820.3990.404-0.1380.4540.4410.6941.0001.0000.9800.4040.0000.000
ERC20_avg_val_sent0.3640.2610.346-0.1680.043-0.040-0.3470.1230.0040.1860.070-0.1550.5020.4490.9860.9810.3970.401-0.1360.4520.4400.7001.0001.0000.9790.4020.0000.000
ERC20_uniq_sent_token_name0.3750.2490.347-0.1620.029-0.051-0.3560.120-0.0040.1830.058-0.1750.5150.4390.9981.0000.4090.416-0.1510.4340.4190.6610.9800.9791.0000.4130.0000.000
ERC20_uniq_rec_token_name0.0590.3990.563-0.607-0.164-0.309-0.471-0.307-0.379-0.253-0.0120.1450.9770.9180.4090.4120.9991.0000.2560.8930.8680.1540.4040.4020.4131.0000.0000.000
ERC20_total_Ether_sent_contract0.0000.0000.3580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.495
ERC20_uniq_sent_addr.10.0000.0000.3580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4951.000

Missing values

2023-04-11T20:25:31.406184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-11T20:25:32.143081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-11T20:25:32.599682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_total_Ether_sent_contractERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_sent_addr.1ERC20_uniq_rec_contract_addrERC20_avg_time_between_sent_tnxERC20_avg_time_between_rec_tnxERC20_avg_time_between_rec_2_tnxERC20_avg_time_between_contract_tnxERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_min_val_sent_contractERC20_max_val_sent_contractERC20_avg_val_sent_contractERC20_uniq_sent_token_nameERC20_uniq_rec_token_name
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36380.000005192.4700018411.570000.030001.050450.598080.500000.500000.500000.500001.794231.294237.00000269.446650.000000.000000.000006.000000.000007.000000.000000.000000.000000.000000.00000241.2878738.492380.000000.000000.000000.000000.000000.000000.000007.00000
41337133.4100031214.68000378118.770000.00100187.0600033.828490.00000187.2530015.04739270.85300270.62790-0.2251022.000001141890.121001141889.490000.000007.000007.000000.000006.000000.000000.000000.000000.000000.00000342426.6000095157.510100.00000342426.00000114188.949000.000000.000000.000005.000006.00000
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202412.9300017.8200061.480000.012350.081310.046830.000000.000000.000000.000000.093660.093663.000007812.000007812.000000.000001.000001.000000.000001.000000.000000.000000.000000.000007812.000007812.000007812.000002812.000005000.000003906.000000.000000.000000.000001.000001.00000
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3313107.700000.82000324.7300011.8610289.1389850.500002.0000087.1984433.66615100.99844101.000000.001560.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000
45460.000000.0000031081.270000.353040.353040.353040.352620.352620.352620.352620.353040.000420.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000
Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_total_Ether_sent_contractERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_sent_addr.1ERC20_uniq_rec_contract_addrERC20_avg_time_between_sent_tnxERC20_avg_time_between_rec_tnxERC20_avg_time_between_rec_2_tnxERC20_avg_time_between_contract_tnxERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_min_val_sent_contractERC20_max_val_sent_contractERC20_avg_val_sent_contractERC20_uniq_sent_token_nameERC20_uniq_rec_token_name
56713073.970009951.5200059124.930000.005000.699650.352320.004190.597710.233970.701900.704650.002751.000001.337000.000000.000000.000001.000000.000001.000000.000000.000000.000000.000001.337001.337001.337000.000000.000000.000000.000000.000000.000000.000001.00000
19640.0000049455.66000197822.630000.000000.000000.000000.000000.000000.000000.000000.000000.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
23469544.0900037026.77000931417.200001.010032.770001.116610.009002.760001.1152022.3039222.332240.028322.0000020.582390.000000.000000.000002.000000.000002.000000.000000.000000.000000.000000.0000020.5823910.291190.000000.000000.000000.000000.000000.000000.000002.00000
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317234.800000.00000104.40000101.00000101.00000101.000001.4705284.4456833.66616100.99849101.000000.001510.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000
19610.00000167.47000134630.730000.001096.529620.7930752.4448452.4448452.4448452.4448440.44679-11.998053.000001500.000000.000000.000000.000002.000000.000003.000000.000000.000000.000000.000000.000001500.00000500.000000.000000.000000.000000.000000.000000.000000.000003.00000
261911129.380003594.92000787773.730000.0050022.831386.133400.0000030.603802.08692133.56285128.80140-4.7614568.00000164963.31020138033.912500.0000019.0000030.000000.0000043.000000.000000.000000.000000.000000.0000033385.172143836.356050.0000025760.000005521.356500.000000.000000.0000017.0000043.00000
17470.000001522.670003045.350000.000000.000000.000000.000000.000000.000000.000000.000000.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39840.000001684.81000291471.380000.0000089.900003.612120.000000.000000.000000.00000624.89731624.897315.00000613.789880.000000.000000.000005.000000.000005.000000.000000.000000.000000.000000.00000600.00000122.757980.000000.000000.000000.000000.000000.000000.000005.00000
428027016.1200044867.83000719316.670000.100009.999532.202900.000003.284001.0274915.4124015.420290.0078915.0000047648.505622929.488420.000002.000008.000000.000007.000000.000000.000000.000000.000000.0000013541.363053665.269660.494782928.993641464.744210.000000.000000.000002.000007.00000

Duplicate rows

Most frequently occurring

Avg_min_between_sent_tnxAvg_min_between_received_tnxTime_Diff_between_first_and_last_(Mins)min_value_receivedmax_value_receivedavg_val_receivedmin_val_sentmax_val_sentavg_val_senttotal_Ether_senttotal_ether_receivedtotal_ether_balanceTotal_ERC20_tnxsERC20_total_Ether_receivedERC20_total_ether_sentERC20_total_Ether_sent_contractERC20_uniq_sent_addrERC20_uniq_rec_addrERC20_uniq_sent_addr.1ERC20_uniq_rec_contract_addrERC20_avg_time_between_sent_tnxERC20_avg_time_between_rec_tnxERC20_avg_time_between_rec_2_tnxERC20_avg_time_between_contract_tnxERC20_min_val_recERC20_max_val_recERC20_avg_val_recERC20_min_val_sentERC20_max_val_sentERC20_avg_val_sentERC20_min_val_sent_contractERC20_max_val_sent_contractERC20_avg_val_sent_contractERC20_uniq_sent_token_nameERC20_uniq_rec_token_name# duplicates
20.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.00000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN16
10.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000000.000001.0000013.370000.000000.000000.000001.000000.000001.000000.000000.000000.000000.0000013.3700013.3700013.370000.000000.000000.000000.000000.000000.000000.000001.000008
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